Summer 2017 – 89840 – PA F310C – Public Policy

Quantitative Foundations for Public Policy

Most of us got interested in public policy because we read the newspaper or watched the news on TV, and got excited about public problems. Maybe you watched a presidential debate, maybe you read David Brooks or Paul Krugman, maybe you just talked about this stuff at the dinner table. (Maybe, like me, you argued politics with your father every night.) But political discussion—and the prospect of participating in that discussion in a meaningful way—is what pulled us in.

As most people talk about it, public policy sounds like an easy choice between stark alternatives based on values. Political leaders, commentators and parties structure this discussion for us; if we know which side we’re on, we know where we stand. It all sounds pretty easy. But if you’re going to do this stuff for a living—and the fact that you’re here means you’re at least considering it—you’ll find that the devil is in the details. Let me give you a couple of examples.

Today's big news: President Trump, flanked by coal miners, signed an executive order intended to roll back former President Obama’s Clean Power Plan, the centerpiece of the federal response to climate change. Trump framed the issue as jobs over regulations, telling the miners, “C’mon fellas. You know what this is? You know what this says? You’re going back to work.” Presumably the order would replace some of the 28,000 coal mining jobs that were lost during the Obama Administration.

Which sounds pretty simple, until you look more closely. Demand for coal is much lower than it was in 2008, mostly because prices for a close substitute—natural gas—have fallen. At its peak in June 2008, gas cost almost $15 per million metric BTUs; today, it’s just under $3. Coal prices dropped, too, but not nearly as much. As a result, hundreds of coal-fired electrical generation plants converted to the cheaper (and cleaner) fuel. Total U.S. coal consumption dropped by 30 percent.

Coal mines are also automating, in a big way. A 2015 study by McKinsey & Co suggested that 99 percent of all “mine cutting and channeling machine operators”—that is, miners—could be replaced by robots. In addition to reducing payrolls, automation also allows mining companies to reduce rising accident insurance and health care costs.

Trump made coal the headline issue, but the effects extend to other energy sectors. The federal government will no longer consider the “social costs of carbon” in permitting and regulatory decisions. Oil and gas drillers can release more methane into the atmosphere. Federal lands closed to coal mining and gas fracking will open up. All of this will reduce short-run costs of gasoline and electric power, perhaps by as much as $100 annually for the average household.

Of course, they will also increase long-run costs associated with air pollution and extreme weather events. The Obama EPA claimed that every dollar spent on clean power regulations would return $4 in health benefits, mostly in terms of reduced asthma and heart attacks, reduced hospitalizations and premature deaths avoided. Cancelling the clean power plan will increase property damage due to increased hurricane intensity and rising sea levels; some climate scientists argued that these costs might be greater than any benefits of cancellation as early as 2025. All of these figures have been disputed by the mining industry, but some costs are almost inevitable.

Even as we speak, someone's trying to sort out who’s right and by how much. That's a good thing.

Here’s another example. My own work concerns economic incentives offered by state and local governments. To make a long story short, if you want growth, you offer incentives to industries you’ve already got—grow what you know. If you want stability, you need to diversify the economy, offering incentives to industries you haven’t got. But the world isn’t as simple as this on/off switch. What’s the right balance of growth and stability for a particular city? Most incentives are wasted on firms that would have expanded or relocated without them. How much should local government be willing to spend on them? Of the gazillion ways to diversify a city’s economy, which is best?

I could go on, but you get the idea. In health care, public safety, education, housing, defense, whatever, it’s pretty easy to throw down a gauntlet. But sooner or later, some apparatchik will have to pick it up. That means making a thousand little decisions. And that means doing the math. For better or worse, that’s our job.

This course is designed to give you the basic mathematical tools needed to understand, work with and eventually create the kind of models that drive public policy decisions. We’ll remind you of your (perhaps forgotten) algebra, run through the basic ideas of differential calculus, and think through the use of probabilities and statistics. You probably won’t feel comfortable going toe-to-toe with Paul Krugman (who, as the Nobel committee reminded us, was an excellent international economist before he became a rich and famous pundit). But you will have the background to take on graduate work in public policy. In a year or two, you may very well be able to take on Krugman. (Certainly you won’t need to repeat yourself so often as he does.)

I’m presuming you haven’t taken a course in calculus or statistics and have forgotten most of your high school algebra. Even if your algebra is bomb-proof, you remember some calculus and you understand some basic statistics, I think you’ll find the way we think about these subjects in the public policy world to be a little more interesting and a lot more practical than the substance of the average math class. Certainly that’s my intention. If this doesn’t seem to be working out, please feel free to let me know along the way.